Abstract

The industrial internet of things (IIoT) is related to the fourth industrial revolution and includes different applications and innovations for the conduction of industrial activities. The introduction of IIoT has revolutionized how manufacturing and industrial units traditionally worked. The current paper aims to analyse the privacy and security issues incorporated in the IIoT and the role of the machine learning (ML) approach for the IIoT. It also analyses industry focused internet of things (IoT) taxonomies and the application of IIoT systems in various smart cities. It is found that IIoT-enabled devices are based on advanced technologies such as big data, robotics, ML, operational technology (OT), and machine-to-machine (M2M) technologies. It helps to intelligently change the behaviour of different environments without any human intervention. ML-based IIoT devices can be used for smart energy, smart transportation, urban planning, and smart city characteristics.

Keyword

Industrial internet of things (IIoT), Machine learning (ML), Smart energy, Smart transportation, Sensors.

Cite this article

Hasan N, Alam M

Refference

[1][1]Iqbal R, Maniak T, Doctor F, Karyotis C. Fault detection and isolation in industrial processes using deep learning approaches. IEEE Transactions on Industrial Informatics. 2019; 15(5):3077-84.

[2][2]Shevchik SA, Masinelli G, Kenel C, Leinenbach C, Wasmer K. Deep learning for in situ and real-time quality monitoring in additive manufacturing using acoustic emission. IEEE Transactions on Industrial Informatics. 2019; 15(9):5194-203.

[3][3]Li Y, Carabelli S, Fadda E, Manerba D, Tadei R, Terzo O. Machine learning and optimization for production rescheduling in industry 4.0. The International Journal of Advanced Manufacturing Technology. 2020; 110:2445-63.

[4][4]Hossain MS, Al-hammadi M, Muhammad G. Automatic fruit classification using deep learning for industrial applications. IEEE Transactions on Industrial Informatics. 2018; 15(2):1027-34.

[5][5]Munirathinam S. Industry 4.0: industrial internet of things (IIOT). In advances in computers 2020 (pp. 129-64). Elsevier.

[6][6]Aceto G, Persico V, Pescapé A. A survey on information and communication technologies for industry 4.0: state-of-the-art, taxonomies, perspectives, and challenges. IEEE Communications Surveys & Tutorials. 2019; 21(4):3467-501.

[7][7]Ray PP. A survey of IoT cloud platforms. Future Computing and Informatics Journal. 2016; 1(1-2):35-46.

[8][8]Wan J, Li J, Imran M, Li D. A blockchain-based solution for enhancing security and privacy in smart factory. IEEE Transactions on Industrial Informatics. 2019; 15(6):3652-60.

[9][9]Mohammed N, Chen R, Fung BC, Yu PS. Differentially private data release for data mining. In proceedings of the 17th international conference on knowledge discovery and data mining 2011 (pp. 493-501). ACM.

[10][10]Hasan N, Alam M. Evolution and insight in industrial internet of things (IIoT): importance and impact. Trust‐Based Communication Systems for Internet of Things Applications. 2022:159-93.

[11][11]https://webstore.ansi.org/preview-pages/ISA/preview_S_990001_2007.pdf. Accessed 11 May 2023.

[12][12]Boyes H, Hallaq B, Cunningham J, Watson T. The industrial internet of things (IIoT): an analysis framework. Computers in Industry. 2018; 101:1-2.

[13][13]Da XL, He W, Li S. Internet of things in industries: a survey. IEEE Transactions on Industrial Informatics. 2014; 10(4):2233-43.

[14][14]Liao Y, Loures ED, Deschamps F. Industrial internet of things: a systematic literature review and insights. IEEE Internet of Things Journal. 2018; 5(6):4515-25.

[15][15]Piccarozzi M, Aquilani B, Gatti C. Industry 4.0 in management studies: a systematic literature review. Sustainability. 2018; 10(10):1-24.

[16][16]Raza S, Faheem M, Guenes M. Industrial wireless sensor and actuator networks in industry 4.0: exploring requirements, protocols, and challenges-a MAC survey. International Journal of Communication Systems. 2019; 32(15):e4074.

[17][17]Leminen S, Rajahonka M, Wendelin R, Westerlund M. Industrial internet of things business models in the machine-to-machine context. Industrial Marketing Management. 2020; 84:298-311.

[18][18]Zolanvari M, Teixeira MA, Gupta L, Khan KM, Jain R. Machine learning-based network vulnerability analysis of industrial internet of things. IEEE Internet of Things Journal. 2019; 6(4):6822-34.

[19][19]Mistry I, Tanwar S, Tyagi S, Kumar N. Blockchain for 5G-enabled IoT for industrial automation: a systematic review, solutions, and challenges. Mechanical Systems and Signal Processing. 2020; 135:106382.

[20][20]Khan WZ, Rehman MH, Zangoti HM, Afzal MK, Armi N, Salah K. Industrial internet of things: recent advances, enabling technologies and open challenges. Computers & Electrical Engineering. 2020; 81:106522.

[21][21]Ur RMH, Yaqoob I, Salah K, Imran M, Jayaraman PP, Perera C. The role of big data analytics in industrial internet of things. Future Generation Computer Systems. 2019; 99:247-59.

[22][22]Heer T, Garcia-morchon O, Hummen R, Keoh SL, Kumar SS, Wehrle K. Security challenges in the IP-based internet of things. Wireless Personal Communications. 2011; 61:527-42.

[23][23]Gubbi J, Buyya R, Marusic S, Palaniswami M. Internet of things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems. 2013; 29(7):1645-60.

[24][24]Cole PH, Ranasinghe DC. Networked RFID systems and lightweight cryptography. 2008.

[25][25]Pöhls HC, Angelakis V, Suppan S, Fischer K, Oikonomou G, Tragos EZ, et al. RERUM: building a reliable IoT upon privacy-and security-enabled smart objects. In wireless communications and networking conference workshops 2014 (pp. 122-7). IEEE.

[26][26]Sadeghi AR, Wachsmann C, Waidner M. Security and privacy challenges in industrial internet of things. In proceedings of the 52nd annual design automation conference 2015 (pp. 1-6). ACM.

[27][27]Atamli AW, Martin A. Threat-based security analysis for the internet of things. In international workshop on secure internet of things 2014 (pp. 35-43). IEEE.

[28][28]Zhang ZK, Cho MC, Wang CW, Hsu CW, Chen CK, Shieh S. IoT security: ongoing challenges and research opportunities. In 7th international conference on service-oriented computing and applications 2014 (pp. 230-4). IEEE.

[29][29]Arends R, Austein R, Larson M, Massey D, Rose S. DNS security introduction and requirements. 2005.

[30][30]http://www.internet-of-things-research.eu/pdf/IERC_Position_Paper_IoT_Governance_Privacy_Security_Final.pdf. Accessed 30 October 2023.

[31][31]Shahid R. Lightweight security solutions for the internet of things (Doctoral Thesis). UMI Order Number: ID Code: 5548. Mälardalen University. 2013.

[32][32]Ziegeldorf JH, Morchon OG, Wehrle K. Privacy in the internet of things: threats and challenges. Security and Communication Networks. 2014; 7(12):2728-42.

[33][33]Qureshi AS, Khan A, Shamim N, Durad MH. Intrusion detection using deep sparse auto-encoder and self-taught learning. Neural Computing and Applications. 2020; 32:3135-47.

[34][34]Lanckriet GR, Ghaoui LE, Bhattacharyya C, Jordan MI. A robust minimax approach to classification. Journal of Machine Learning Research. 2002; 3:555-82.

[35][35]Xu H, Caramanis C, Mannor S. Robust regression and lasso. Advances in Neural Information Processing Systems. 2008.

[36][36]Paschali M, Conjeti S, Navarro F, Navab N. Generalizability vs. robustness: adversarial examples for medical imaging. International conference on medical image computing and computer-assisted intervention 2018 (pp. 493-501). Springer.

[37][37]Tang C, Li W, Wang P, Wang L. Online human action recognition based on incremental learning of weighted covariance descriptors. Information Sciences. 2018; 467:219-37.

[38][38]Ristin M, Guillaumin M, Gall J, Van GL. Incremental learning of random forests for large-scale image classification. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2015; 38(3):490-503.

[39][39]Du H, Teng S, Yang M, Zhu Q. Intrusion detection system based on improved SVM incremental learning. In international conference on artificial intelligence and computational intelligence 2009 (pp. 23-8). IEEE.

[40][40]Miorandi D, Sicari S, De PF, Chlamtac I. Internet of things: vision, applications and research challenges. Ad Hoc Networks. 2012; 10(7):1497-516.

[41][41]Alkhabbas F, Spalazzese R, Davidsson P. Characterizing internet of things systems through taxonomies: a systematic mapping study. Internet of Things. 2019; 7:100084.

[42][42]Wang Y, Li J, Wang HH. Cluster and cloud computing framework for scientific metrology in flow control. Cluster Computing. 2019; 22:1189-98.

[43][43]Mukherjee M, Shu L, Wang D. Survey of fog computing: fundamental, network applications, and research challenges. IEEE Communications Surveys & Tutorials. 2018; 20(3):1826-57.

[44][44]Yu W, Liang F, He X, Hatcher WG, Lu C, Lin J, et al. A survey on the edge computing for the internet of things. IEEE Access. 2017; 6:6900-19.

[45][45]Satyanarayanan M. The emergence of edge computing. Computer. 2017; 50(1):30-9.

[46][46]Stergiou C, Psannis KE, Kim BG, Gupta B. Secure integration of IoT and cloud computing. Future Generation Computer Systems. 2018; 78:964-75.

[47][47]Yang C, Huang Q, Li Z, Liu K, Hu F. Big data and cloud computing: innovation opportunities and challenges. International Journal of Digital Earth. 2017; 10(1):13-53.

[48][48]Li S, Maddah-ali MA, Yu Q, Avestimehr AS. A fundamental tradeoff between computation and communication in distributed computing. IEEE Transactions on Information Theory. 2017; 64(1):109-28.

[49][49]El-sayed H, Sankar S, Prasad M, Puthal D, Gupta A, Mohanty M, et al. Edge of things: the big picture on the integration of edge, IoT and the cloud in a distributed computing environment. IEEE Access. 2017; 6:1706-17.

[50][50]Kanawaday A, Sane A. Machine learning for predictive maintenance of industrial machines using IoT sensor data. In 8th international conference on software engineering and service science 2017 (pp. 87-90). IEEE.

[51][51]Hammi B, Khatoun R, Zeadally S, Fayad A, Khoukhi L. IoT technologies for smart cities. IET Networks. 2018; 7(1):1-3.

[52][52]Marinakis V, Doukas H. An advanced IoT-based system for intelligent energy management in buildings. Sensors. 2018; 18(2):1-16.

[53][53]Paul A, Chilamkurti N, Daniel A, Rho S. Chapter 8-big data collision analysis framework. Intelligent Vehicular Networks and Communications. 2017: 177-84.

[54][54]Distefano S, Merlino G, Puliafito A, Cerotti D, Dautov R. Crowdsourcing and stigmergic approaches for (Swarm) intelligent transportation systems. In human centered computing: third international conference, HCC 2017, Kazan, Russia, Revised Selected Papers 3 2018 (pp. 616-26). Springer International Publishing.

[55][55]Zantalis F, Koulouras G, Karabetsos S, Kandris D. A review of machine learning and IoT in smart transportation. Future Internet. 2019; 11(4):1-23.

[56][56]Chowdhury DN, Agarwal N, Laha AB, Mukherjee A. A vehicle-to-vehicle communication system using IoT approach. In second international conference on electronics, communication and aerospace technology 2018 (pp. 915-9). IEEE.

[57][57]Geetha S, Cicilia D. IoT enabled intelligent bus transportation system. In 2nd international conference on communication and electronics systems 2017 (pp. 7-11). IEEE.

[58][58]Hasan N, Alam M. Envisaging industrial perspective demand response using machine learning. In proceedings of data analytics and management: ICDAM 2021, 2022 (pp. 331-42). Springer Singapore.

[59][59]Nausicaa J. Smart street lighting system using IoT and cloud computing. International Journal for Research in Applied Science & Engineering Technology. 2021; 9(6):5014-18.

[60][60]Ryder B, Wortmann F. Autonomously detecting and classifying traffic accident hotspots. In proceedings of the international joint conference on pervasive and ubiquitous computing and proceedings, international symposium on wearable computers 2017 (pp. 365-70). ACM.

[61][61]Rani P, Kumar R, Jain A, Lamba R. Taxonomy of machine learning algorithms and its applications. Journal of Computational and Theoretical Nanoscience. 2020; 17(6):2508-13.

[62][62]Berrar D. Bayes theorem and naive Bayes classifier. Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics. 2018; 403:412.

[63][63]Setiawan B, Djanali S, Ahmad T. A study on intrusion detection using centroid-based classification. Procedia Computer Science. 2017; 124:672-81.

[64][64]Sun W, Liu J, Yue Y. AI-enhanced offloading in edge computing: when machine learning meets industrial IoT. IEEE Network. 2019; 33(5):68-74.

[65][65]Hussain T, Muhammad K, Del SJ, Baik SW, De AVH. Intelligent embedded vision for summarization of multiview videos in IIoT. IEEE Transactions on Industrial Informatics. 2019; 16(4):2592-602.

[66][66]Oracevic A, Dilek S, Ozdemir S. Security in internet of things: a survey. In international symposium on networks, computers and communications 2017 (pp. 1-6). IEEE.

[67][67]Patel KK, Patel SM, Scholar P. Internet of things-IOT: definition, characteristics, architecture, enabling technologies, application & future challenges. International Journal of Engineering Science and Computing. 2016; 6(5):6122-31.

[68][68]Udoh IS, Kotonya G. Developing IoT applications: challenges and frameworks. IET Cyber‐Physical Systems: Theory & Applications. 2018; 3(2):65-72.

[69][69]Mahdavinejad MS, Rezvan M, Barekatain M, Adibi P, Barnaghi P, Sheth AP. Machine learning for internet of things data analysis: a survey. Digital Communications and Networks. 2018; 4(3):161-75.

[70][70]Rahman A, Chakraborty C, Anwar A, Karim MR, Islam MJ, Kundu D, et al. SDN–IoT empowered intelligent framework for industry 4.0 applications during COVID-19 pandemic. Cluster Computing. 2021:1-8.

[71][71]Li J, Jin J, Lyu L, Yuan D, Yang Y, Gao L, et al. A fast and scalable authentication scheme in IOT for smart living. Future Generation Computer Systems. 2021; 117:125-37.

[72][72]Ray PP, Dash D, De D. Edge computing for internet of things: a survey, e-healthcare case study and future direction. Journal of Network and Computer Applications. 2019; 140:1-22.

[73][73]Che F, Ahmed A, Ahmed QZ, Zaidi SA, Shakir MZ. Machine learning based approach for indoor localization using ultra-wide bandwidth (UWB) system for industrial internet of things (IIoT). In international conference on UK-China emerging technologies 2020 (pp. 1-4). IEEE.

[74][74]Liu X, Yu W, Liang F, Griffith D, Golmie N. Toward deep transfer learning in industrial internet of things. IEEE Internet of Things Journal. 2021; 8(15):12163-75.

[75][75]Mateo FW, Redchuk A. The emergence of new business and operating models under the industrial digital paradigm. industrial internet of things, platforms, and artificial intelligence. machine learning. Journal of Mechanics Engineering and Automation. 2021; 11(2):54-60.

[76][76]Liu M, Yu FR, Teng Y, Leung VC, Song M. Performance optimization for blockchain-enabled industrial Internet of things (IIoT) systems: a deep reinforcement learning approach. IEEE Transactions on Industrial Informatics. 2019; 15(6):3559-70.

[77][77]Rathee G, Ahmad F, Sandhu R, Kerrache CA, Azad MA. On the design and implementation of a secure blockchain-based hybrid framework for industrial internet-of-things. Information Processing & Management. 2021; 58(3):102526.

[78][78]Christou IT, Kefalakis N, Zalonis A, Soldatos J. Predictive and explainable machine learning for industrial internet of things applications. In international conference on distributed computing in sensor systems 2020 (pp. 213-8). IEEE.

[79][79]Velusamy N, Al-turjman F, Kumar R, Ramakrishnan J. A framework for task allocation in IoT-oriented industrial manufacturing systems. Computer Networks. 2021; 190:107971.

[80][80]Ambika P. Machine learning and deep learning algorithms on the industrial internet of things (IIoT). Advances in Computers. 2020; 117(1):321-38.

[81][81]Zhou H, She C, Deng Y, Dohler M, Nallanathan A. Machine learning for massive industrial internet of things. IEEE Wireless Communications. 2021; 28(4):81-7.

[82][82]Qiu C, Yu FR, Yao H, Jiang C, Xu F, Zhao C. Blockchain-based software-defined industrial internet of things: a dueling deep ${Q} $-learning approach. IEEE Internet of Things Journal. 2018; 6(3):4627-39.

[83][83]Luo X, Liu J, Zhang D, Chang X. A large-scale web QoS prediction scheme for the industrial internet of things based on a kernel machine learning algorithm. Computer Networks. 2016; 101:81-9.

[84][84]Wang M, Xu CA, Lin Y, Lu Z, Sun J, Gui G. A distributed sensor system based on cloud-edge-end network for industrial internet of things. Future Internet. 2023; 15(5):1-17.

[85][85]Yang T, Hao W, Yang Q, Wang W. Cloud-edge coordinated traffic anomaly detection for industrial cyber-physical systems. Expert Systems with Applications. 2023:120668.

[86][86]Su W, Xu G, He Z, Machica IK, Quimno V, Du Y, et al. Cloud-edge computing-based ICICOS framework for industrial automation and artificial intelligence: a survey. Journal of Circuits, Systems and Computers. 2023:2350168.

[87][87]Maurya M, Panigrahi I, Dash D, Malla C. Intelligent fault diagnostic system for rotating machinery based on IoT with cloud computing and artificial intelligence techniques: a review. Soft Computing. 2023:1-8.

[88][88]Abid A, Jemili F, Korbaa O. Real-time data fusion for intrusion detection in industrial control systems based on cloud computing and big data techniques. Cluster Computing. 2023:1-22.

[89][89]Lu Y, Yang L, Yang SX, Hua Q, Sangaiah AK, Guo T, et al. An intelligent deterministic scheduling method for ultralow latency communication in edge enabled industrial internet of things. IEEE Transactions on Industrial Informatics. 2022; 19(2):1756-67.

[90][90]Le TT, Oktian YE, Kim H. XGBoost for imbalanced multiclass classification-based industrial internet of things intrusion detection systems. Sustainability. 2022; 14(14):1-21.

[91][91]Anajemba JH, Iwendi C, Razzak I, Ansere JA, Okpalaoguchi IM. A counter-eavesdropping technique for optimized privacy of wireless industrial IoT communications. IEEE Transactions on Industrial Informatics. 2022; 18(9):6445-54.

[92][92]Ren H, Anicic D, Runkler TA. Towards semantic management of on-device applications in industrial IoT. ACM Transactions on Internet Technology. 2022; 22(4):1-30.

[93][93]Ahmed YA, Huda S, Al-rimy BA, Alharbi N, Saeed F, Ghaleb FA, et al. A weighted minimum redundancy maximum relevance technique for ransomware early detection in industrial IoT. Sustainability. 2022; 14(3):1-15.

[94][94]Feng Y, Zhang W, Luo X, Zhang B. A consortium blockchain-based access control framework with dynamic orderer node selection for 5G-enabled industrial IoT. IEEE Transactions on Industrial Informatics. 2021; 18(4):2840-8.

[95][95]Krishnan P, Jain K, Achuthan K, Buyya R. Software-defined security-by-contract for blockchain-enabled MUD-aware industrial IoT edge networks. IEEE Transactions on Industrial Informatics. 2021; 18(10):7068-76.

[96][96]Khowaja SA, Khuwaja P. Q-learning and LSTM based deep active learning strategy for malware defense in industrial IoT applications. Multimedia Tools and Applications. 2021; 80(10):14637-63.

[97][97]Khan AI, Al-badi A. Open source machine learning frameworks for industrial internet of things. Procedia Computer Science. 2020; 170:571-7.